Consumer API basics in Kafka - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Kafka consumer poll code?

Consider the following Kafka consumer code snippet in Java. What will be printed to the console?

Kafka

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("group.id", "test-group");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("my-topic"));

ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
for (ConsumerRecord<String, String> record : records) {
    System.out.println(record.key() + ":" + record.value());
}
consumer.close();

A Throws a SerializationException due to missing deserializer configuration

B Throws a NullPointerException because poll duration is too short

C Prints all key:value pairs from 'my-topic' available within 100ms poll duration

D Prints nothing if no messages are available within 100ms poll duration

💡 Hint

Think about what happens if the topic has no messages ready during the poll.

The poll method waits up to the specified duration for messages. If no messages are available, it returns an empty ConsumerRecords object, so the loop prints nothing.

🧠 Conceptual

intermediate

What does the 'group.id' property control in Kafka consumer?

In Kafka consumer configuration, what is the role of the group.id property?

A It sets the maximum number of messages to fetch per poll

B It identifies the consumer group for load balancing and offset management

C It specifies the Kafka broker address to connect to

D It defines the serialization format for message keys

💡 Hint

Think about how Kafka manages multiple consumers reading from the same topic.

The group.id identifies the consumer group. Kafka uses this to distribute partitions among consumers and track offsets for fault tolerance.

🔧 Debug

advanced

Why does this Kafka consumer code cause a runtime error?

Examine the following Kafka consumer code snippet. Why does it throw a SerializationException at runtime?

Kafka

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("group.id", "group1");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
// Missing value.deserializer configuration

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("topic1"));
ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
for (ConsumerRecord<String, String> record : records) {
    System.out.println(record.key() + ":" + record.value());
}
consumer.close();

A Because the value.deserializer property is missing, Kafka cannot deserialize message values

B Because the topic name is incorrect, causing topic not found error

C Because the poll duration is too short, causing timeout exceptions

D Because the group.id is invalid, causing consumer group join failure

💡 Hint

Check the deserializer configurations carefully.

Kafka needs both key and value deserializers to convert bytes to objects. Missing value.deserializer causes a SerializationException when reading message values.

📝 Syntax

advanced

Which option correctly commits offsets synchronously in Kafka consumer?

Which of the following code snippets correctly commits offsets synchronously after processing records?

A consumer.commitAsync();

B consumer.commitSync(Duration.ofSeconds(1));

C consumer.commitSync();

D consumer.commitAsync(Duration.ofSeconds(1));

💡 Hint

Look for the method that commits offsets synchronously without parameters.

The commitSync() method commits offsets synchronously and throws exceptions on failure. The other methods are asynchronous or invalid.

🚀 Application

expert

How many partitions will be assigned to each consumer in this group?

Suppose a Kafka topic has 6 partitions and there are 3 consumers in the same consumer group subscribed to this topic. How many partitions will each consumer be assigned?

A Each consumer will be assigned 2 partitions

B Each consumer will be assigned 3 partitions

C One consumer will get all 6 partitions, others get none

D Partitions are assigned randomly with no guarantee of distribution

💡 Hint

Think about how Kafka balances partitions among consumers in a group.

Kafka evenly distributes partitions among consumers in a group. With 6 partitions and 3 consumers, each gets 2 partitions.